Generative Model-driven Structure Aligning Discriminative Embeddings for Transductive Zero-shot Learning

TL;DR

This paper introduces a neural network-based model for zero-shot learning that aligns visual and semantic data in a discriminative latent space, utilizing transductive learning to mitigate domain shift and improve performance on standard benchmarks.

Contribution

A novel transductive approach using a conditional variational auto-encoder to generate semantic features for unseen classes, enhancing zero-shot learning performance.

Findings

Superior performance on benchmark datasets AWA1, AWA2, CUB, SUN, FLO, and APY.

Effective in low labeled data regimes for ZSL.

Reduces projection domain shift using unlabeled unseen class data.

Abstract

Zero-shot Learning (ZSL) is a transfer learning technique which aims at transferring knowledge from seen classes to unseen classes. This knowledge transfer is possible because of underlying semantic space which is common to seen and unseen classes. Most existing approaches learn a projection function using labelled seen class data which maps visual data to semantic data. In this work, we propose a shallow but effective neural network-based model for learning such a projection function which aligns the visual and semantic data in the latent space while simultaneously making the latent space embeddings discriminative. As the above projection function is learned using the seen class data, the so-called projection domain shift exists. We propose a transductive approach to reduce the effect of domain shift, where we utilize unlabeled visual data from unseen classes to generate corresponding semantic features for unseen class visual samples. While these semantic features are initially generated using a conditional variational auto-encoder, they are used along with the seen class data to improve the projection function. We experiment on both inductive and transductive setting of ZSL and generalized ZSL and show superior performance on standard benchmark datasets AWA1, AWA2, CUB, SUN, FLO, and APY. We also show the efficacy of our model in the case of extremely less labelled data regime on different datasets in the context of ZSL.